Data transformation with dplyr

Data transformation refers to a collection of techniques for performing row-level treatment on the raw data using dplyr functions. In this section, we will cover five fundamental functions for data transformation: filter(), arrange(), mutate(), select(), and top_n().

Slicing the dataset using the filter() function

One of the biggest highlights of the tidyverse ecosystem is the pipe operator, %>%, which provides the statement before it as the contextual input for the statement after it. Using the pipe operator gives us better clarity in terms of code structuring, besides saving the need to type multiple repeated contextual statements. Let’s go through an exercise on how to use the pipe operator to slice the iris dataset using the filter() function.

Exercise 2.02 – filtering using the pipe operator

For this exercise, we have been asked to keep only the setosa species in the iris dataset using the pipe operator and the filter...